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--- |
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language: en |
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tags: |
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- GECToR_gotutiyan |
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- grammatical error correction |
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--- |
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Only non-commercial purposes. |
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# gector sample |
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This is an unofficial pretrained model of GECToR ([Omelianchuk+ 2020](https://aclanthology.org/2020.bea-1.16/)). |
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### How to use |
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The code is avaliable from https://github.com/gotutiyan/gector. |
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CLI |
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```sh |
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python predict.py --input <raw text file> --restore_dir gotutiyan/gector-roberta-base-5k --out <path to output file> |
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``` |
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API |
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```py |
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from transformers import AutoTokenizer |
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from gector.modeling import GECToR |
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from gector.predict import predict, load_verb_dict |
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import torch |
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model_id = 'gotutiyan/gector-roberta-base-5k' |
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model = GECToR.from_pretrained(model_id) |
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if torch.cuda.is_available(): |
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model.cuda() |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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encode, decode = load_verb_dict('data/verb-form-vocab.txt') |
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srcs = [ |
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'This is a correct sentence.', |
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'This are a wrong sentences' |
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] |
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corrected = predict( |
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model, tokenizer, srcs, |
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encode, decode, |
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keep_confidence=0.0, |
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min_error_prob=0.0, |
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n_iteration=5, |
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batch_size=2, |
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) |
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print(corrected) |
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``` |
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